Let's say I have designed an ML model that can take video input of a dog running around and give the breed of the dog as output. However, I do not want to wait for the video to finish before it is input into my model. I want something like the following to happen:

I am casually taking a video of my backyard when mid-way through a dog runs past the camera. Immediately, my model should identify (a) a dog has appeared within view and (b) the dog is a Labrador Retriever.

In an attempt to achieve the above, I have the following questions:

  1. Do I need to train a new model that detects when a dog has appeared within view?
  2. How can I make my model such that the input is continuous and that the model keeps running providing instant output?

Note: This question was originally posted on Stack Overflow. It was closed as a consequence of it being unfitting to the site. Hence, I am uploading the question here. Link to the original post here.

  • $\begingroup$ Sorry to push you from pillar to post, but I think the topic here is best suited to Data Science Stack Exchange. It is related to data engineering and machine learning engineer skillsets. The answer will depend on lots of specifics - where you are running the detector, how that is connected to your camera (have you written something embedded on the camera, or are you sending a feed to a nearby computer over wifi?), and what frameworks you are using (Tensorflow? OpenCV?) There won't be a satisfactory "generic" answer. So please add those details if you decide to ask again. $\endgroup$ Jul 19 at 12:59
  • $\begingroup$ @NeilSlater Thanks! I will add those details and ask my question on Data Science Stack Exchange. $\endgroup$ Jul 19 at 15:50

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